Deep RL leverages the representational power of deep learning to tackle the RL problem. We define a deep RL system as any system that solves an RL problem (i.e. maximizes long-term reward), using representations that are themselves learned by a deep neural network (rather than stipulated by the designer). Typical, deep RL systems use a deep neural network to compute a non-linear mapping from perceptual inputs to action-values [1]} or action-probabilities [2]}), as well as reinforcement learning signals that update the weights in this network, often via backpropagation, in order to produce better estimates of reward or to increase the frequency of highly-rewarded actions (Figure REF c).
